Data Cleaning - Geospatial Data

import pandas as pd
import os
import geopandas as gpd
import folium
from folium.plugins import HeatMap
from IPython.display import display, HTML, IFrame
from io import BytesIO
import base64
# specify the directory containing the CSV files
directory = '../../data/quarterly_financials'

# create an empty list to store the dataframes
dfs = []

# loop over the CSV files in the directory
for filename in os.listdir(directory):
    if filename.endswith('.csv'):
        # read the CSV file into a dataframe and append it to the list
        path = os.path.join(directory, filename)
        df = pd.read_csv(path)
        dfs.append(df)

# concatenate the dataframes into a single dataframe
quarterly_financials = pd.concat(dfs, ignore_index=True)
# sort
quarterly_financials = quarterly_financials.sort_values(
    by=['REPDTE', 'CERT'], ascending=[False, True])
quarterly_financials = quarterly_financials.reset_index(drop=True)
# print the combined dataframe
display(quarterly_financials.shape)
display(quarterly_financials.head())
(1014549, 16)
ZIP BKCLASS EQR REPDTE DEPDOM ASSET STNAME EQ NAME CITY ADDRESS ENDEFYMD CERT ESTYMD LIAB ID
0 2111 SM 8.918529 20221231 163284000.0 298020000 MASSACHUSETTS 26579000.0 STATE STREET BANK&TRUST CO BOSTON 1 LINCOLN ST 99991231.0 14 17920101 271441000.0 14_20221231
1 36830 SM 6.446081 20221231 952037.0 1023366 ALABAMA 65967.0 AUBURNBANK AUBURN 100 N GAY ST 99991231.0 35 19070103 957399.0 35_20221231
2 36732 NM 7.323604 20221231 407949.0 444822 ALABAMA 32577.0 ROBERTSON BANKING CO DEMOPOLIS 216 N WALNUT AVE 99991231.0 39 18700101 412245.0 39_20221231
3 36867 NM -3.191064 20221231 266874.0 265272 ALABAMA -8465.0 PHENIX-GIRARD BANK PHENIX CITY 801 13TH ST 99991231.0 41 19040504 273737.0 41_20221231
4 36401 NM 7.197104 20221231 70649.0 76239 ALABAMA 5487.0 BANK OF EVERGREEN EVERGREEN 146 W FRONT ST 99991231.0 49 19320901 70752.0 49_20221231 
display(len(quarterly_financials['BKCLASS'].unique()))
display(quarterly_financials['BKCLASS'].unique())
7
array(['SM', 'NM', 'N', 'SI', 'SB', 'SL', 'OI'], dtype=object)

Zip coordinates

# bring in
zip_coordinates = pd.read_csv(
    '../../data/coordinate_data/2022_Gaz_zcta_national.txt', sep='\t')

# remove whitespace in col names
zip_coordinates.columns = [col.strip() for col in zip_coordinates.columns]

# drop unnecesary columns
zip_coordinates.drop(['ALAND', 'ALAND_SQMI', 'AWATER',
                     'AWATER_SQMI'], axis=1, inplace=True)

# check
display(zip_coordinates.shape)
display(zip_coordinates.head())
(33791, 3)
GEOID INTPTLAT INTPTLONG
0 601 18.180555 -66.749961
1 602 18.361945 -67.175597
2 603 18.457399 -67.124867
3 606 18.158327 -66.932928
4 610 18.293960 -67.127182 
display(len(quarterly_financials['ZIP'].unique()))
10756

Combine

# Rename the 'GEOID' column in 'zip_coordinates' to match the 'ZIP' column in 'quarterly_financials'
zip_coordinates = zip_coordinates.rename(columns={'GEOID': 'ZIP'})

# Merge the two dataframes on the 'ZIP' column
merged_df = quarterly_financials.merge(zip_coordinates, on='ZIP', how='left')

# Create a new column 'coordinates' with the combined 'INTPTLAT' and 'INTPTLONG' columns as a tuple
merged_df['zip_coordinates'] = list(
    zip(merged_df['INTPTLAT'], merged_df['INTPTLONG']))

# Update the 'quarterly_financials' dataframe with the new 'coordinates' column
quarterly_financials = merged_df

# Print the updated 'quarterly_financials' dataframe
display(quarterly_financials.shape)
display(quarterly_financials.head())
(1014549, 19)
ZIP BKCLASS EQR REPDTE DEPDOM ASSET STNAME EQ NAME CITY ADDRESS ENDEFYMD CERT ESTYMD LIAB ID INTPTLAT INTPTLONG zip_coordinates
0 2111 SM 8.918529 20221231 163284000.0 298020000 MASSACHUSETTS 26579000.0 STATE STREET BANK&TRUST CO BOSTON 1 LINCOLN ST 99991231.0 14 17920101 271441000.0 14_20221231 42.350680 -71.060527 (42.35068, -71.060527)
1 36830 SM 6.446081 20221231 952037.0 1023366 ALABAMA 65967.0 AUBURNBANK AUBURN 100 N GAY ST 99991231.0 35 19070103 957399.0 35_20221231 32.534872 -85.493755 (32.534872, -85.493755)
2 36732 NM 7.323604 20221231 407949.0 444822 ALABAMA 32577.0 ROBERTSON BANKING CO DEMOPOLIS 216 N WALNUT AVE 99991231.0 39 18700101 412245.0 39_20221231 32.417456 -87.892213 (32.417456, -87.892213)
3 36867 NM -3.191064 20221231 266874.0 265272 ALABAMA -8465.0 PHENIX-GIRARD BANK PHENIX CITY 801 13TH ST 99991231.0 41 19040504 273737.0 41_20221231 32.498054 -85.023590 (32.498054, -85.02359)
4 36401 NM 7.197104 20221231 70649.0 76239 ALABAMA 5487.0 BANK OF EVERGREEN EVERGREEN 146 W FRONT ST 99991231.0 49 19320901 70752.0 49_20221231 31.468970 -86.950426 (31.46897, -86.950426) 
# removed unmatched zips
quarterly_financials.dropna(subset=['INTPTLAT'], inplace=True)

# drop unnecesary columns
quarterly_financials.drop(['INTPTLAT', 'INTPTLONG', 'ENDEFYMD', 'CERT',
                          'ESTYMD', 'LIAB', 'ID', 'ADDRESS', 'EQ', 'ZIP'], axis=1, inplace=True)

# convert numerical values to millions (source is in thousands)
quarterly_financials['DEPDOM'] = quarterly_financials['DEPDOM'].divide(1000)
quarterly_financials['ASSET'] = quarterly_financials['ASSET'].divide(1000)

# rename
quarterly_financials.rename(columns={
    'BKCLASS': 'bank_class',
    'REPDTE': 'report_date',
    'DEPDOM': 'deposits_mill',
    'ASSET': 'assets_mill',
    'STNAME': 'state',
    'NAME': 'name',
    'CITY': 'city'
}, inplace=True)

display(quarterly_financials.head())
bank_class EQR report_date deposits_mill assets_mill state name city zip_coordinates
0 SM 8.918529 20221231 163284.000 298020.000 MASSACHUSETTS STATE STREET BANK&TRUST CO BOSTON (42.35068, -71.060527)
1 SM 6.446081 20221231 952.037 1023.366 ALABAMA AUBURNBANK AUBURN (32.534872, -85.493755)
2 NM 7.323604 20221231 407.949 444.822 ALABAMA ROBERTSON BANKING CO DEMOPOLIS (32.417456, -87.892213)
3 NM -3.191064 20221231 266.874 265.272 ALABAMA PHENIX-GIRARD BANK PHENIX CITY (32.498054, -85.02359)
4 NM 7.197104 20221231 70.649 76.239 ALABAMA BANK OF EVERGREEN EVERGREEN (31.46897, -86.950426) 
# change to title case
quarterly_financials[['state', 'name', 'city']] = quarterly_financials[[
    'state', 'name', 'city']].apply(lambda x: x.str.title())

# format date
quarterly_financials['report_date'] = pd.to_datetime(
    quarterly_financials['report_date'], format='%Y%m%d')

# set to float
quarterly_financials['deposits_mill'] = quarterly_financials['deposits_mill'].astype(
    float)
quarterly_financials['assets_mill'] = quarterly_financials['assets_mill'].astype(
    float)


display(quarterly_financials.head())
bank_class EQR report_date deposits_mill assets_mill state name city zip_coordinates
0 SM 8.918529 2022-12-31 163284.000 298020.000 Massachusetts State Street Bank&Trust Co Boston (42.35068, -71.060527)
1 SM 6.446081 2022-12-31 952.037 1023.366 Alabama Auburnbank Auburn (32.534872, -85.493755)
2 NM 7.323604 2022-12-31 407.949 444.822 Alabama Robertson Banking Co Demopolis (32.417456, -87.892213)
3 NM -3.191064 2022-12-31 266.874 265.272 Alabama Phenix-Girard Bank Phenix City (32.498054, -85.02359)
4 NM 7.197104 2022-12-31 70.649 76.239 Alabama Bank Of Evergreen Evergreen (31.46897, -86.950426) 
quarterly_financials['bank_class'] = quarterly_financials['bank_class'].replace({
    'N':  'Commercial bank, national charter, Fed member',
    'NM': 'Commercial bank, state charter, Fed non-member',
    'OI': 'Insured U.S. branch of a foreign chartered institution',
    'SB': 'Federal savings banks',
    'SI': 'State chartered stock savings banks',
    'SL': 'State chartered stock savings and loan association',
    'SM': 'Commercial bank, state charter, Fed member',
    'NC': 'Noninsured non-deposit commercial bank',
    'NS': 'Noninsured stock savings bank',
    'CU': 'State or federally chartered credit union',
})

display(quarterly_financials.shape)
display(quarterly_financials.head())
(979599, 9)
bank_class EQR report_date deposits_mill assets_mill state name city zip_coordinates
0 Commercial bank, state charter, Fed member 8.918529 2022-12-31 163284.000 298020.000 Massachusetts State Street Bank&Trust Co Boston (42.35068, -71.060527)
1 Commercial bank, state charter, Fed member 6.446081 2022-12-31 952.037 1023.366 Alabama Auburnbank Auburn (32.534872, -85.493755)
2 Commercial bank, state charter, Fed non-member 7.323604 2022-12-31 407.949 444.822 Alabama Robertson Banking Co Demopolis (32.417456, -87.892213)
3 Commercial bank, state charter, Fed non-member -3.191064 2022-12-31 266.874 265.272 Alabama Phenix-Girard Bank Phenix City (32.498054, -85.02359)
4 Commercial bank, state charter, Fed non-member 7.197104 2022-12-31 70.649 76.239 Alabama Bank Of Evergreen Evergreen (31.46897, -86.950426) 
quarterly_financials['zip_coordinates'].isna().sum()
0

Growth of Assets Per State

# convert financials to geodataframe
gdf = gpd.GeoDataFrame(quarterly_financials, geometry=gpd.points_from_xy(quarterly_financials.zip_coordinates.apply(
    lambda p: p[1]), quarterly_financials.zip_coordinates.apply(lambda p: p[0])))

# define a function to get the aggregate assets


def plot_bank_assets_by_date(date):

    # filter geodataframe and group by state
    filtered_gdf = gdf[gdf['report_date'] == date]
    state_assets = filtered_gdf.groupby(
        'state')['assets_mill'].sum().reset_index()

    # create base folium map
    m = folium.Map(location=[37.8, -96], zoom_start=3)

    # function to scale the bubbles
    def scale_bubble_size(assets):
        return assets / 50000

    # plot the bubbles
    for index, row in state_assets.iterrows():
        state_data = filtered_gdf[filtered_gdf['state'] == row['state']]
        state_centroid = state_data.unary_union.centroid
        folium.CircleMarker(
            location=[state_centroid.y, state_centroid.x],
            radius=scale_bubble_size(row['assets_mill']),
            color='#118C4F',
            fill=True,
            fill_color='#118C4F',
            fill_opacity=0.5,
            popup=f"State: {row['state']}<br>Total Assets: {row['assets_mill']:.0f}<br>Date: {date}"
        ).add_to(m)

    # show
    return m


# define dates to show in the grid
unique_dates = ['1992-12-31', '2002-12-31', '2012-12-31', '2022-12-31']

# init maps
maps = []
for date in unique_dates:
    maps.append(plot_bank_assets_by_date(pd.to_datetime(date)))


# for rendering in HTML
def folium_map_to_iframe_data(m, map_name):
    map_path = f'geospatial_data_html/{map_name}.html'
    m.save(map_path)
    iframe = IFrame(src=map_path, width=350, height=300)
    return iframe


# init html structure
html_structure = """
<style>
    .map-container {
        display: flex;
        flex-wrap: wrap;
    }
    .map-box {
        width: 50%;
        padding: 5px;
        box-sizing: border-box;
    }
    .map-title {
        text-align: center;
        font-weight: bold;
    }
</style>
<div class="map-container">
"""

# add iframes to the html
for i, m in enumerate(maps):
    map_name = f'map_{i}'
    iframe_data = folium_map_to_iframe_data(m, map_name)
    date = unique_dates[i]
    html_structure += f'<div class="map-box"><div class="map-title">{date}</div>{iframe_data._repr_html_()}</div>'

# close html
html_structure += '</div>'
# show
display(HTML(html_structure))


# alternative for checking in jupyter notebook
if True == False:  # only if required

    def folium_map_to_iframe_data(m):
        data = BytesIO()
        m.save(data, close_file=False)
        data.seek(0)
        b64 = base64.b64encode(data.read()).decode("utf-8")
        return f'<iframe src="data:text/html;base64,{b64}" width="350" height="300"></iframe>'

    table_html = '<table><tr>'
    for i, m in enumerate(maps):
        iframe_data = folium_map_to_iframe_data(m)
        table_html += f'<td><h3>{unique_dates[i]}</h3>{iframe_data}</td>'
        if (i + 1) % 2 == 0:
            table_html += '</tr><tr>'
    table_html += '</tr></table>'

    display(HTML(table_html))
1992-12-31
2002-12-31
2012-12-31
2022-12-31

Individual Points

Heatmap all dates

Latest date only

# get the latest date
latest_date = quarterly_financials['report_date'].max()

# filter dataframe
latest_quarterly_financials = quarterly_financials[quarterly_financials['report_date'] == latest_date]

# create base folium map
map = folium.Map(location=[37.8, -96], zoom_start=4)

# heatmap data setup
heatmap_data = []
for index, row in latest_quarterly_financials.iterrows():
    coordinates = row['zip_coordinates']
    assets_mill = row['assets_mill']
    heatmap_data.append((*coordinates, assets_mill))

# add heatmap to folium
heatmap = HeatMap(heatmap_data, radius=13, max_zoom=13, gradient={
                  0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 1: 'red'})
map.add_child(heatmap)

# scale the bubbles
def scale_bubble_size(assets):
    return assets / 50000

# add the circles with tootltips
for index, row in latest_quarterly_financials.iterrows():
    coordinates = row['zip_coordinates']
    assets_mill = row['assets_mill']
    bank_name = row['name']
    tooltip_text = f"Bank: {bank_name}<br>Coordinates: {coordinates}<br>Assets (millions): {assets_mill}"
    folium.CircleMarker(
        location=coordinates,
        radius=scale_bubble_size(assets_mill),
        color='#118C4F',
        fill=True,
        fill_color='#118C4F',
        fill_opacity=0.5,
        tooltip=tooltip_text,
    ).add_to(map)

# for rendering on Quarto
map.save('geospatial_data_html/heatmap.html')
IFrame(src='geospatial_data_html/heatmap.html', width=700, height=600)

# for rendering in Jupyter Notebook
display(map)
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